Build a music analytics application in Python using Streamlit and the Spotify API. Learn to create interactive data visualizations and insights from Spotify's extensive music database. Explore features like mood analysis, song recommendations, and artist information. Gain hands-on experience integrating the Spotipy library to access Spotify's Web API and develop a user-friendly interface with Streamlit. Follow along as the tutorial guides you through setting up the development environment, implementing key features, and deploying the final application to GitHub. Perfect for developers looking to combine data analysis with music technology.
Music Analytics App in Python with Streamlit and Spotify
Overview
Syllabus
- Video Start
- Final Analytics App Demo
- Content Introduction
- Part 1 of Tutorial Recap
- Resources and Guidance
- Tutorial Starts
- Streamlit app development starts
- Spotify Library Integration
- Keyword search integration
- Song Features integration
- Song Recommendation Integration
- Song Recommendation visualization
- Album Visual Art Connection
- Album and song Search
- Artist Integration
- Artist top songs integration
- Final streamlit application demo
- Final code review
- Code push to GitHub
- Recap
- Credits
Taught by
Prodramp
Related Courses
-
Develop Music Analytics App with Spotify API in Python
-
Building a Spotify Clone with Flutter, Python and FastAPI - Full Stack Development with MVVM Architecture
-
Create Streamlit Data Validation App With Deepchecks Test Suites and Pandas-Profiling Reports
-
Build a Python Website in 15 Minutes with Streamlit
-
GitHub Star History Tracker in Python - End-to-End Streamlit Tutorial
-
Build a TikTok Data Science App with Streamlit and Python - Data Science Project
